In recent years, NMR logging has become very important for purposes of formation is evaluation and is one of the preferred methods for determining formation parameters. Improvements in NMR logging tools, as well as advances in data analysis and interpretation, allow log analysts to generate detailed reservoir description reports, including clay-bound and capillary-bound related porosity, estimates of the amounts of bound and free fluids, fluid types (i.e., oil, gas and water), permeability and other properties of interest.
Fluid typing and quantification is one of the primary objectives of using NMR logging in many formation evaluation programs. NMR based fluid typing techniques are based on the contrast of spin-relaxation relaxation time, T1, spin-spin relaxation time, T2, and/or diffusivity, D, of different fluids in the porous space of formation rocks. Therefore, the quality of detecting and resolving different fluids lie on a proper data acquisition method to capture the contrast and a proper data processing method to recover the contrast with minimal distortion by noise or processing artifacts. To accomplish this, modern NMR well logging tools and core analysis instruments acquire a large amount data with different acquisition parameters and pulse sequences, resulting in multiple echo trains of thousands elements each. The fluid contrast is captured but imbedded in the time evolution of NMR signal amplitudes acquired with these variations of parameters and pulse sequences.
Often, an inversion algorithm is applied to derive the distributions of T1, T2, and D, or a subset of these, from the time-domain NMR measurements. The size of the inversion matrixes can become significantly large, thus resulting in poor system performance or system failure. Therefore, it is essential to address these memory management and computational efficiency challenges associated with processing inversion of NMR data.